Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for operating a Point Of Sale (“POS”) system, comprising: performing operations, by the POS system, to obtain article information from a label or tag coupled to an article to be purchased; performing operations, by the POS system, to machine learn at least information about a physical or chemical condition of the article to be purchased using at least one of imaging, sensing, and short range communications; determining by the POS system if the article is accepted for purchase based at least on the article information and the machine learned information about the physical or chemical condition of the article; completing a purchase transaction by the POS system if a determination is made that the article is accepted for purchase; and performing operations by the POS system to deactivate a security tag coupled to the article or actuate a detachment mechanism of the security tag, if the article was successfully purchased during the purchase transaction.
This invention relates to a Point of Sale (POS) system that enhances transaction security and quality control by analyzing both article information and physical or chemical conditions of items being purchased. The system obtains article details from a label or tag attached to the item, such as a barcode or RFID tag. Additionally, the POS system uses machine learning techniques to assess the item's condition through imaging, sensing, or short-range communications. For example, it may detect spoilage in perishable goods, verify product authenticity, or check for physical damage. The system then determines whether to accept the item for purchase based on both the label information and the learned condition data. If accepted, the transaction proceeds, and the system deactivates or detaches a security tag to prevent theft. This approach improves fraud detection, ensures product quality, and automates security measures at checkout. The method integrates traditional POS functions with advanced sensing and machine learning to provide a more robust and secure purchasing process.
2. The method according to claim 1 , wherein the purchase transaction is completed by communicating article information and payment information from a POS station to a remote transaction system via a network connection.
A method for completing a purchase transaction involves transmitting article information and payment details from a point-of-sale (POS) station to a remote transaction system over a network. The POS station captures data about the items being purchased, such as product identifiers and quantities, along with payment information provided by the customer, such as credit card details or digital wallet credentials. This data is securely sent to a centralized transaction system, which processes the payment and confirms the transaction. The remote system may include servers hosted by a financial institution, payment processor, or merchant service provider. The network connection can be established via wired or wireless communication protocols, ensuring real-time or near-real-time transaction processing. This approach streamlines checkout processes by automating data transfer and reducing manual input, improving efficiency and accuracy in retail or e-commerce environments. The method may also include additional steps such as verifying payment authorization, generating receipts, and updating inventory records based on the transaction details. The system ensures secure transmission of sensitive financial data, often using encryption and compliance with industry standards like PCI DSS. This method is particularly useful in modern retail systems where fast, reliable, and secure transactions are essential.
3. The method according to claim 1 , wherein the machined learned information indicates whether the article is at least one of damaged, expired and spoiled.
A system and method for analyzing articles using machine learning to determine their condition. The technology addresses the challenge of efficiently and accurately assessing the state of articles, such as perishable goods, to identify issues like damage, expiration, or spoilage. The method involves capturing data from the article, such as images, sensor readings, or other relevant measurements, and processing this data through a trained machine learning model. The model analyzes the input data to classify the article's condition, providing an output that indicates whether the article is damaged, expired, or spoiled. This classification helps in quality control, inventory management, and automated decision-making processes. The machine learning model is trained on a dataset of labeled examples, where each example includes input data and a corresponding condition label. The training process optimizes the model's parameters to minimize errors in classification. Once trained, the model can be deployed to analyze new articles in real-time or batch processing environments. The system may also include additional features, such as generating alerts or triggering actions based on the detected condition of the article. This approach improves efficiency and accuracy in article inspection, reducing manual labor and human error.
4. The method according to claim 3 , wherein the determination is made that the article is accepted for purchase when the machined learned information indicates that the article is not one or more of damaged, expired and spoiled.
This invention relates to automated article inspection and acceptance systems, particularly for determining whether an article should be accepted for purchase based on its condition. The system uses machine learning to analyze visual or sensor data of an article to detect defects such as damage, expiration, or spoilage. The machine learning model is trained on labeled data representing acceptable and unacceptable articles, allowing it to classify new articles with high accuracy. When the system processes an article, it generates a prediction indicating whether the article is damaged, expired, or spoiled. If the machine learning output confirms the article is free from these issues, the system automatically accepts it for purchase. This approach reduces manual inspection errors and improves efficiency in retail, logistics, or quality control environments. The system may integrate with point-of-sale or inventory management systems to streamline workflows. The machine learning model can be continuously updated with new data to improve accuracy over time. This method ensures only high-quality articles are accepted, reducing waste and customer dissatisfaction.
5. The method according to claim 1 , further comprising performing operations by the POS system to machine learn an identity of a person purchasing the article.
This invention relates to point-of-sale (POS) systems that enhance transaction security and personalization by machine learning the identity of a person purchasing an article. The system captures transaction data, including payment methods, purchase history, and biometric or behavioral patterns, to build a profile of the purchaser. By analyzing this data, the system identifies recurring patterns and associates them with specific individuals, even if they use different payment methods or accounts. The learned identity is then used to authenticate transactions, detect fraud, or personalize recommendations. The system may also integrate with external databases or identity verification services to cross-reference and refine its predictions. This approach improves security by reducing reliance on static credentials and enhances user experience by enabling tailored services based on recognized identities. The method is particularly useful in retail, e-commerce, and financial services where accurate identity verification is critical. The system continuously updates its models to adapt to new behaviors or fraud patterns, ensuring ongoing accuracy and reliability.
6. The method according to claim 5 , further comprising performing operations by the POS system to determine whether the person is allergic to the article.
This invention relates to point-of-sale (POS) systems that enhance transaction security and user safety by verifying whether a customer is allergic to a purchased item. The system integrates with existing POS infrastructure to assess allergy risks during checkout. When a customer initiates a purchase, the POS system cross-references the selected item against a database of known allergens. The system then checks the customer's profile, which may include pre-recorded allergy information or real-time input, to determine if the item poses a risk. If an allergy is detected, the system alerts the customer and may suggest alternatives or block the transaction. The method ensures that customers with allergies are protected during purchases, reducing health risks and liability for retailers. The system may also update allergy records based on customer feedback or new allergen data, improving accuracy over time. This solution is particularly useful in food retail, pharmaceuticals, and other industries where allergen exposure is a concern. The invention combines transaction processing with health safety features, providing a seamless and secure shopping experience.
7. The method according to claim 6 , wherein the article is accepted for purchase when a determination is made that the person is not allergic to the article.
This invention relates to a system for verifying the safety of articles, such as food or personal care products, before purchase by individuals with allergies. The method involves scanning an article to identify its ingredients or components, then cross-referencing this information with a database of known allergens. The system checks whether the person attempting to purchase the article has any allergies that match the identified ingredients. If no allergies are detected, the purchase is approved. If a match is found, the system may block the purchase or provide an alert. The method ensures that individuals with allergies do not accidentally purchase items that could trigger a reaction. The system may also allow users to input their allergy profiles or retrieve them from a stored record. The scanning process can be performed using a mobile device or a dedicated scanner, and the allergen database is regularly updated to include new allergen information. The invention aims to prevent allergic reactions by providing real-time verification before purchase.
8. The method according to claim 1 , wherein the machined learned information specifies the article's appearance.
A method for processing visual data of an article using machine learning to determine its appearance. The method involves capturing an image of the article, analyzing the image with a trained machine learning model, and extracting learned information that defines the article's visual characteristics. The machine learning model is trained on a dataset of labeled images to recognize and classify features such as color, texture, shape, and other visual attributes. The extracted information is then used to generate a digital representation of the article's appearance, which can be applied in applications like object recognition, quality control, or augmented reality. The method ensures accurate and consistent identification of the article's visual properties by leveraging machine learning techniques to interpret complex visual data. This approach improves upon traditional image processing methods by incorporating learned patterns and reducing reliance on predefined rules, leading to more robust and adaptable visual analysis.
9. The method according to claim 8 , wherein the article is accepted for purchase when the article's appearance matches that described by a given product description.
A system and method for verifying the authenticity and condition of physical articles, such as products, using visual inspection and comparison against a reference description. The method involves capturing an image of the article, analyzing its visual characteristics, and comparing them to a predefined product description. The product description includes details about the article's expected appearance, such as color, texture, shape, and any identifying marks or labels. The system processes the captured image to extract relevant visual features and determines whether the article's appearance matches the description. If the match is confirmed, the article is accepted for purchase or further processing. This method ensures that only articles meeting the specified criteria are approved, reducing the risk of counterfeit or damaged goods entering the supply chain. The system may also include additional verification steps, such as checking for tampering or verifying the presence of required security features. The method is particularly useful in e-commerce, logistics, and retail environments where visual inspection is critical for quality control and fraud prevention.
10. The method according to claim 9 , further comprising setting a flag for providing access to at least one of a product description for the article, an expiration date for the article, and tag sensor data associated with the article to the purchaser of the article via a receipt or electronic message, when the article is accepted for purchase.
This invention relates to a system for managing and providing access to product information and sensor data associated with purchased articles. The problem addressed is the lack of convenient access to detailed product information, expiration dates, and sensor data (such as RFID or NFC tag data) for consumers after purchase. The solution involves a method where, upon acceptance of an article for purchase, a flag is set to grant the purchaser access to relevant data. This data includes product descriptions, expiration dates, and sensor data linked to the article. The information is delivered via a receipt or electronic message, ensuring the purchaser can easily retrieve it. The method integrates with a broader system that may include scanning the article, verifying its authenticity, and processing payment. The flag-setting mechanism ensures that only authorized purchasers receive the data, enhancing security and user experience. This approach improves transparency and convenience for consumers while maintaining data integrity.
11. A method for operating a Point Of Sale (“POS”) system, comprising: performing operations by the POS system to obtain article information from a label or tag coupled to an article to be purchased; performing operations by the POS system to machine learn at least information about a physical or chemical condition of the article to be purchased using at least one of imaging, sensing, and short range communications; determining by the POS system if the article is accepted for purchase based at least on the article information and the machine learned information about the physical or chemical condition of the article; and completing a purchase transaction by the POS system if a determination is made that the article is accepted for purchase.
This invention relates to a Point of Sale (POS) system that enhances transaction security and quality control by analyzing both article information and physical or chemical conditions of items before purchase. The system obtains article details from a label or tag attached to the item, such as a barcode or RFID tag. Additionally, the POS system uses machine learning techniques to assess the item's condition through imaging (e.g., visual inspection for damage), sensing (e.g., temperature, humidity, or weight measurements), or short-range communications (e.g., reading embedded sensors). The system then evaluates whether the item meets acceptance criteria based on the combined data from the label and the machine-learned condition analysis. If the item is accepted, the purchase transaction proceeds. This approach ensures that only items meeting specified quality or safety standards are sold, reducing fraud, spoilage, or defective product sales. The method integrates traditional POS functions with advanced analytics to improve transaction reliability and consumer trust.
12. A POS system, comprising: a processor; a non-transitory computer-readable storage medium comprising programming instructions that are configured to cause the processor to implement a method for performing a purchase transaction, wherein the programming instructions comprise instructions to: obtain article information from a label or tag coupled to an article to be purchased; machine learn at least information about a physical or chemical condition of the article to be purchased using at least one of imaging, sensing, and short range communications; determine if the article is accepted for purchase based at least on the article information and the machine learned information about the physical or chemical condition of the article; complete a purchase transaction if a determination is made that the article is accepted for purchase; and deactivate a security tag coupled to the article or actuate a detachment mechanism of the security tag, if the article was successfully purchased during the purchase transaction.
This invention relates to a point-of-sale (POS) system designed to enhance transaction security and quality control by integrating machine learning with traditional POS functions. The system addresses the problem of verifying article conditions during purchase, ensuring only acceptable items are sold while preventing theft or fraud. The POS system includes a processor and a non-transitory storage medium containing programming instructions. These instructions enable the system to obtain article information from a label or tag attached to the item. The system then uses machine learning to analyze the article's physical or chemical condition through imaging, sensing, or short-range communications. For example, it may detect spoilage, damage, or tampering in perishable goods or verify authenticity in high-value items. Based on the article information and the machine-learned condition data, the system determines whether the article meets purchase criteria. If accepted, the transaction proceeds. If the purchase is successful, the system deactivates a security tag or triggers a detachment mechanism to prevent unauthorized removal. This ensures only verified items leave the premises, reducing losses from theft or counterfeit sales. The system combines traditional POS functions with advanced analytics to improve transaction integrity, particularly for items requiring condition verification.
13. The POS system according to claim 12 , wherein the purchase transaction is completed by communicating article information and payment information from a POS station to a remote transaction system via a network connection.
A point-of-sale (POS) system is designed to facilitate retail transactions by processing payments and managing inventory. A key challenge in modern POS systems is ensuring secure and efficient communication between the POS station and remote transaction systems, such as payment processors or inventory databases, to complete transactions accurately and reliably. This POS system addresses this challenge by enabling the completion of purchase transactions through secure communication of article information and payment details from the POS station to a remote transaction system over a network connection. The article information includes details about the items being purchased, such as product identifiers, quantities, and prices. The payment information encompasses data required for processing payments, such as payment method, card details, or digital wallet credentials. The network connection ensures real-time data exchange, allowing the remote transaction system to verify payment authorization, update inventory, and generate receipts. This approach enhances transaction security, reduces processing delays, and improves overall system reliability by leveraging networked communication for seamless transaction handling.
14. The POS system according to claim 12 , wherein the machined learned information indicates whether the article is at least one of damaged, expired and spoiled.
A point-of-sale (POS) system is designed to process transactions for articles, such as food items, at retail locations. A challenge in such systems is accurately identifying the condition of articles, particularly perishable goods, to prevent sales of damaged, expired, or spoiled items. This system addresses this issue by incorporating machine learning to analyze article data and determine its condition. The system includes a machine learning model trained to evaluate article attributes, such as visual features, expiration dates, or sensor data, to classify whether the article is damaged, expired, or spoiled. The model processes this information and generates a condition status, which is then used to guide transaction processing. For example, if an article is identified as spoiled, the system may block the sale or alert staff. The system may also integrate with inventory management to track and remove affected items. The machine learning model is trained using labeled datasets of articles with known conditions, allowing it to learn patterns associated with damage, expiration, or spoilage. The system may further refine its accuracy over time by incorporating feedback from user inputs or additional sensor data. This approach enhances operational efficiency by reducing manual inspections and improving customer satisfaction by ensuring only acceptable articles are sold.
15. The POS system according to claim 14 , wherein the determination is made that the article is accepted for purchase when the machined learned information indicates that the article is not one or more of damaged, expired and spoiled.
A point-of-sale (POS) system for automated article acceptance in retail transactions. The system uses machine learning to analyze article data, such as images, barcodes, or sensor readings, to determine whether an article is suitable for purchase. The system checks for defects, expiration, or spoilage by comparing the article data against learned criteria. If the machine learning model confirms the article is undamaged, not expired, and not spoiled, the system automatically accepts it for purchase. This eliminates manual inspection, reducing errors and speeding up checkout processes. The system may integrate with existing POS hardware, such as scanners or cameras, to capture article data. The machine learning model is trained on historical data of acceptable and unacceptable articles, continuously improving accuracy. The system may also flag articles for further review if the model's confidence is low. This approach enhances efficiency in retail environments, particularly for perishable goods or high-volume transactions.
16. The POS system according to claim 12 , wherein the programming instructions comprise instructions to machine learn an identity of a person purchasing the article.
A point-of-sale (POS) system is designed to process transactions and manage retail operations. A specific challenge in such systems is accurately identifying customers, particularly for personalized services, loyalty programs, or fraud prevention. This invention addresses that challenge by incorporating machine learning capabilities into the POS system to recognize and learn the identity of a person purchasing an article. The POS system includes hardware components such as a payment terminal, display, and input devices, along with software programming instructions. The machine learning functionality is integrated into the system's programming to analyze customer data, such as purchase history, biometric inputs, or behavioral patterns, to predict or confirm the identity of the purchaser. This allows the system to adapt over time, improving recognition accuracy without requiring manual updates. The system may also include features for processing payments, generating receipts, and managing inventory. The machine learning component enhances these functions by enabling personalized recommendations, automated loyalty rewards, or fraud detection based on learned customer identities. The overall design ensures seamless integration of identity recognition into existing POS workflows, improving efficiency and customer experience.
17. The POS system according to claim 16 , wherein the programming instructions comprise instructions to determine whether the person is allergic to the article.
A point-of-sale (POS) system is designed to enhance transaction security and user experience by integrating biometric authentication and personalized data processing. The system captures biometric data, such as facial recognition or fingerprint scans, to verify the identity of a person conducting a transaction. This authentication process ensures that only authorized individuals can complete purchases, reducing fraud and unauthorized access. The system further includes programming instructions to analyze the transaction details, such as the items being purchased, and cross-reference them with stored user data. This allows the system to provide personalized recommendations, loyalty rewards, or other tailored services based on the user's purchase history and preferences. Additionally, the system includes functionality to determine whether the person is allergic to any of the articles being purchased. By accessing stored health or allergy information, the system can alert the user or the seller if a potential allergen is detected in the transaction. This feature enhances safety by preventing accidental exposure to harmful substances. The system may also include a user interface that displays transaction details, authentication status, and any relevant alerts, such as allergy warnings. The interface ensures that users are informed throughout the transaction process, improving transparency and trust. The integration of biometric authentication, personalized data processing, and health-related alerts makes this POS system a comprehensive solution for secure and user-friendly transactions.
18. The POS system according to claim 17 , wherein the article is accepted for purchase when a determination is made that the person is not allergic to the article.
A point-of-sale (POS) system for retail transactions includes a mechanism to verify whether a customer has allergies to a selected product before completing a purchase. The system identifies the customer, retrieves their allergy profile from a database, and checks if the selected product contains any allergens listed in the profile. If no allergens are detected, the system authorizes the purchase. If allergens are found, the system either blocks the transaction or prompts the customer for confirmation. The allergy profile may be stored locally or accessed remotely, and the system can update the profile based on customer input or medical records. The POS system integrates with existing retail hardware, such as barcode scanners and payment terminals, to streamline the process. This invention addresses the risk of allergic reactions in retail environments by preventing accidental purchases of harmful products, enhancing customer safety and compliance with health regulations. The system is particularly useful in pharmacies, grocery stores, and other retail settings where allergen awareness is critical.
19. The POS system according to claim 12 , wherein the machined learned information specifies the article's appearance.
A point-of-sale (POS) system is designed to enhance transaction processing by integrating machine learning to improve accuracy and efficiency. The system includes a machine learning model trained to recognize and classify articles based on their visual characteristics. This model processes images or video feeds from a camera to identify items presented for purchase, reducing the need for manual input or barcode scanning. The machine learning information specifies the article's appearance, enabling the system to match visual data with product databases for accurate identification. The system may also include a display for presenting transaction details, a payment interface for processing payments, and a communication module for transmitting data to a central server. The machine learning model is trained using labeled datasets of product images, ensuring reliable recognition across various lighting conditions and orientations. This approach streamlines checkout processes, minimizes errors, and enhances customer experience by automating item recognition. The system is particularly useful in retail environments where speed and accuracy are critical, such as supermarkets, convenience stores, and self-checkout kiosks.
20. The POS system according to claim 19 , wherein the article is accepted for purchase when the article's appearance matches that described by a given product description.
A point-of-sale (POS) system is designed to automate the verification and acceptance of articles for purchase based on their visual appearance. The system addresses the challenge of ensuring that the correct product is being purchased, particularly in scenarios where barcodes or other identifiers may be missing, damaged, or unreliable. The system uses image recognition technology to compare the visual characteristics of an article presented for purchase against a predefined product description. The product description includes visual attributes such as color, shape, size, and texture, which are used to determine whether the article matches the expected product. If the article's appearance aligns with the product description, the system accepts it for purchase. This verification process enhances accuracy in retail transactions, reduces errors, and improves efficiency by minimizing the need for manual inspection. The system may also integrate with existing POS infrastructure, such as barcode scanners and payment terminals, to provide a seamless checkout experience. By leveraging image recognition, the system ensures that customers receive the correct items while streamlining the transaction process.
21. The POS system according to claim 20 , wherein the programming instructions comprise instructions to set a flag for providing access to at least one of a product description for the article, an expiration date for the article, and tag sensor data associated with the article to the purchaser of the article via a receipt or electronic message, when the article is accepted for purchase.
A point-of-sale (POS) system is designed to enhance transaction processing by providing additional product information to purchasers. The system includes programming instructions that set a flag to enable access to specific details about an article being purchased. These details may include a product description, an expiration date, and sensor data associated with the article. When the article is accepted for purchase, the system delivers this information to the purchaser through a receipt or an electronic message. This functionality allows customers to receive detailed product insights at the time of purchase, improving transparency and convenience. The system may also include features for processing payments, verifying product authenticity, and managing inventory, ensuring a seamless transaction experience. By integrating these capabilities, the POS system enhances the purchasing process by providing comprehensive product information directly to the buyer.
Unknown
June 30, 2020
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